Computational Fluid Dynamics Study of the Wake of a High-Clearance Agricultural Boom Sprayer
Bibliographic record
Abstract
Self propelled agricultural sprayers are commonly found on farms in Saskatchewan. These vehicles are used to spray pesticides onto crops to increase the productivity of the field. Spray drift occurs when pesticides are carried away from their target. It has been estimated that up to 30% of all pesticides sprayed onto a crop will drift. The literature contains multiple studies on how particles released from a nozzle will travel, but there is a lack of research towards understanding how the airflow patterns around an agricultural sprayer might affect spray drift. The present thesis research modeled the airflow around a John Deere 4830 agricultural sprayer using computational fluid dynamics (CFD), with a focus on the sprayer wake. Since agricultural sprayers are large vehicles, the numerical grid must include enough elements to realistically model the wake of the sprayer, while keeping the number of elements low enough to make the simulations possible in a reasonable amount of time. Several grids with different element size and type were investigated to meet this requirement. Benchmarking studies were performed on a circular cylinder to determine the performance of different grids. The best performing grids were then tested on a small section of the sprayer boom. In the sample boom tests the smaller elements provided more detail, but there were stability issues, the coarse elements were ultimately chosen as they performed realistically. The full-scale simulations were performed using the STAR-CCM+ commercial CFD code. Using the grid developed earlier, three simulations were performed on the agricultural sprayer. The three simulations represented different boom heights and sprayer travel speeds to determine how different operating conditions may affect the airflow around the sprayer. The simulations showed that the wake of a sprayer has four different zones, corresponding to different parts of the vehicle and boom geometry. The wake of the sprayer was shown to be largest directly behind the vehicle. The downstream extent of the wake decreases along the boom up to the folding knuckle where the size increases again due to the increased blockage of the flow. The simulations showed that increasing the sprayer travel speed and increasing the boom height both caused significant increases to the turbulence intensity in the wake of the sprayer and in the region near the nozzles. This in turn could increase the potential for spray drift occurring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".